Overview

Dataset statistics

Number of variables50
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.8 MiB
Average record size in memory400.0 B

Variable types

CAT36
NUM13
BOOL1

Warnings

examide has constant value "101766" Constant
citoglipton has constant value "101766" Constant
medical_specialty has a high cardinality: 73 distinct values High cardinality
diag_1 has a high cardinality: 717 distinct values High cardinality
diag_2 has a high cardinality: 749 distinct values High cardinality
diag_3 has a high cardinality: 790 distinct values High cardinality
number_emergency is highly skewed (γ1 = 22.85558215) Skewed
encounter_id has unique values Unique
num_procedures has 46652 (45.8%) zeros Zeros
number_outpatient has 85027 (83.6%) zeros Zeros
number_emergency has 90383 (88.8%) zeros Zeros
number_inpatient has 67630 (66.5%) zeros Zeros

Reproduction

Analysis started2020-12-10 01:22:32.233889
Analysis finished2020-12-10 01:24:25.728897
Duration1 minute and 53.5 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

encounter_id
Real number (ℝ≥0)

UNIQUE

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165201645.6
Minimum12522
Maximum443867222
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:25.981007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median152388987
Q3230270887.5
95-th percentile378962843
Maximum443867222
Range443854700
Interquartile range (IQR)145309693.5

Descriptive statistics

Standard deviation102640296
Coefficient of variation (CV)0.6213031087
Kurtosis-0.1020713932
Mean165201645.6
Median Absolute Deviation (MAD)70921143
Skewness0.6991415513
Sum1.681191067e+13
Variance1.053503036e+16
MonotocityNot monotonic
2020-12-09T17:24:26.233775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
962109421< 0.1%
 
899438461< 0.1%
 
3843069861< 0.1%
 
946501561< 0.1%
 
831567841< 0.1%
 
26744821< 0.1%
 
2813458441< 0.1%
 
1936162741< 0.1%
 
3555080241< 0.1%
 
1659738181< 0.1%
 
Other values (101756)101756> 99.9%
 
ValueCountFrequency (%) 
125221< 0.1%
 
157381< 0.1%
 
166801< 0.1%
 
282361< 0.1%
 
357541< 0.1%
 
ValueCountFrequency (%) 
4438672221< 0.1%
 
4438571661< 0.1%
 
4438541481< 0.1%
 
4438477821< 0.1%
 
4438475481< 0.1%
 

patient_nbr
Real number (ℝ≥0)

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330400.69
Minimum135
Maximum189502619
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:26.476111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.75
Q123413221
median45505143
Q387545949.75
95-th percentile111480273
Maximum189502619
Range189502484
Interquartile range (IQR)64132728.75

Descriptive statistics

Standard deviation38696359.35
Coefficient of variation (CV)0.7122413759
Kurtosis-0.3473720444
Mean54330400.69
Median Absolute Deviation (MAD)32950134
Skewness0.4712807224
Sum5.528987557e+12
Variance1.497408227e+15
MonotocityNot monotonic
2020-12-09T17:24:26.694014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8878589140< 0.1%
 
4314090628< 0.1%
 
2319902123< 0.1%
 
166029323< 0.1%
 
8822754023< 0.1%
 
2364340522< 0.1%
 
8442861322< 0.1%
 
9270935121< 0.1%
 
2339848820< 0.1%
 
9060980420< 0.1%
 
Other values (71508)10152499.8%
 
ValueCountFrequency (%) 
1352< 0.1%
 
3781< 0.1%
 
7291< 0.1%
 
7741< 0.1%
 
9271< 0.1%
 
ValueCountFrequency (%) 
1895026191< 0.1%
 
1894814781< 0.1%
 
1894451271< 0.1%
 
1893658641< 0.1%
 
1893510951< 0.1%
 

race
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
Caucasian
76099 
AfricanAmerican
19210 
?
 
2273
Hispanic
 
2037
Other
 
1506
ValueCountFrequency (%) 
Caucasian7609974.8%
 
AfricanAmerican1921018.9%
 
?22732.2%
 
Hispanic20372.0%
 
Other15061.5%
 
Asian6410.6%
 
2020-12-09T17:24:27.147226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:27.270360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:27.436059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length9
Mean length9.849507694
Min length1

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
Female
54708 
Male
47055 
Unknown/Invalid
 
3
ValueCountFrequency (%) 
Female5470853.8%
 
Male4705546.2%
 
Unknown/Invalid3< 0.1%
 
2020-12-09T17:24:27.604675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:27.707648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:27.841275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length6
Mean length5.075496728
Min length4

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
[70-80)
26068 
[60-70)
22483 
[50-60)
17256 
[80-90)
17197 
[40-50)
9685 
Other values (5)
9077 
ValueCountFrequency (%) 
[70-80)2606825.6%
 
[60-70)2248322.1%
 
[50-60)1725617.0%
 
[80-90)1719716.9%
 
[40-50)96859.5%
 
[30-40)37753.7%
 
[90-100)27932.7%
 
[20-30)16571.6%
 
[10-20)6910.7%
 
[0-10)1610.2%
 
2020-12-09T17:24:27.993466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:28.188016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:28.378277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length7.025863255
Min length6

weight
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
?
98569 
[75-100)
 
1336
[50-75)
 
897
[100-125)
 
625
[125-150)
 
145
Other values (5)
 
194
ValueCountFrequency (%) 
?9856996.9%
 
[75-100)13361.3%
 
[50-75)8970.9%
 
[100-125)6250.6%
 
[125-150)1450.1%
 
[25-50)970.1%
 
[0-25)48< 0.1%
 
[150-175)35< 0.1%
 
[175-200)11< 0.1%
 
>2003< 0.1%
 
2020-12-09T17:24:28.553189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:28.705193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:28.898761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length1
Mean length1.217096083
Min length1

admission_type_id
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024006053
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:29.037314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.44540283
Coefficient of variation (CV)0.7141296972
Kurtosis1.942476114
Mean2.024006053
Median Absolute Deviation (MAD)0
Skewness1.591984327
Sum205975
Variance2.08918934
MonotocityNot monotonic
2020-12-09T17:24:29.386871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
15399053.1%
 
31886918.5%
 
21848018.2%
 
652915.2%
 
547854.7%
 
83200.3%
 
721< 0.1%
 
410< 0.1%
 
ValueCountFrequency (%) 
15399053.1%
 
21848018.2%
 
31886918.5%
 
410< 0.1%
 
547854.7%
 
ValueCountFrequency (%) 
83200.3%
 
721< 0.1%
 
652915.2%
 
547854.7%
 
410< 0.1%
 

discharge_disposition_id
Real number (ℝ≥0)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.715641766
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:29.730863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.280165509
Coefficient of variation (CV)1.421064204
Kurtosis6.003346764
Mean3.715641766
Median Absolute Deviation (MAD)0
Skewness2.563066993
Sum378126
Variance27.88014781
MonotocityNot monotonic
2020-12-09T17:24:29.931139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
16023459.2%
 
31395413.7%
 
61290212.7%
 
1836913.6%
 
221282.1%
 
2219932.0%
 
1116421.6%
 
511841.2%
 
259891.0%
 
48150.8%
 
Other values (16)22342.2%
 
ValueCountFrequency (%) 
16023459.2%
 
221282.1%
 
31395413.7%
 
48150.8%
 
511841.2%
 
ValueCountFrequency (%) 
281390.1%
 
275< 0.1%
 
259891.0%
 
2448< 0.1%
 
234120.4%
 

admission_source_id
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.754436649
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:30.091371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.064080834
Coefficient of variation (CV)0.7062517293
Kurtosis1.744989372
Mean5.754436649
Median Absolute Deviation (MAD)0
Skewness1.029934878
Sum585606
Variance16.51675303
MonotocityNot monotonic
2020-12-09T17:24:30.261345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
75749456.5%
 
12956529.1%
 
1767816.7%
 
431873.1%
 
622642.2%
 
211041.1%
 
58550.8%
 
31870.2%
 
201610.2%
 
91250.1%
 
Other values (7)43< 0.1%
 
ValueCountFrequency (%) 
12956529.1%
 
211041.1%
 
31870.2%
 
431873.1%
 
58550.8%
 
ValueCountFrequency (%) 
252< 0.1%
 
2212< 0.1%
 
201610.2%
 
1767816.7%
 
142< 0.1%
 

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395986872
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:30.403894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.985107767
Coefficient of variation (CV)0.6790529304
Kurtosis0.8502508405
Mean4.395986872
Median Absolute Deviation (MAD)2
Skewness1.133998719
Sum447362
Variance8.910868383
MonotocityNot monotonic
2020-12-09T17:24:30.579689image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
31775617.4%
 
21722416.9%
 
11420814.0%
 
41392413.7%
 
599669.8%
 
675397.4%
 
758595.8%
 
843914.3%
 
930022.9%
 
1023422.3%
 
Other values (4)55555.5%
 
ValueCountFrequency (%) 
11420814.0%
 
21722416.9%
 
31775617.4%
 
41392413.7%
 
599669.8%
 
ValueCountFrequency (%) 
1410421.0%
 
1312101.2%
 
1214481.4%
 
1118551.8%
 
1023422.3%
 

payer_code
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
?
40256 
MC
32439 
HM
6274 
SP
5007 
BC
4655 
Other values (13)
13135 
ValueCountFrequency (%) 
?4025639.6%
 
MC3243931.9%
 
HM62746.2%
 
SP50074.9%
 
BC46554.6%
 
MD35323.5%
 
CP25332.5%
 
UN24482.4%
 
CM19371.9%
 
OG10331.0%
 
Other values (8)16521.6%
 
2020-12-09T17:24:30.784573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-09T17:24:30.938625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.60442584
Min length1

medical_specialty
Categorical

HIGH CARDINALITY

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
?
49949 
InternalMedicine
14635 
Emergency/Trauma
7565 
Family/GeneralPractice
7440 
Cardiology
5352 
Other values (68)
16825 
ValueCountFrequency (%) 
?4994949.1%
 
InternalMedicine1463514.4%
 
Emergency/Trauma75657.4%
 
Family/GeneralPractice74407.3%
 
Cardiology53525.3%
 
Surgery-General30993.0%
 
Nephrology16131.6%
 
Orthopedics14001.4%
 
Orthopedics-Reconstructive12331.2%
 
Radiologist11401.1%
 
Other values (63)83408.2%
 
2020-12-09T17:24:31.150556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2020-12-09T17:24:31.342710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length8
Mean length8.612670243
Min length1

num_lab_procedures
Real number (ℝ≥0)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.09564098
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:31.519713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.67436225
Coefficient of variation (CV)0.4565278947
Kurtosis-0.2450735189
Mean43.09564098
Median Absolute Deviation (MAD)13
Skewness-0.2365439206
Sum4385671
Variance387.0805299
MonotocityNot monotonic
2020-12-09T17:24:31.739475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132083.2%
 
4328042.8%
 
4424962.5%
 
4523762.3%
 
3822132.2%
 
4022012.2%
 
4621892.2%
 
4121172.1%
 
4221132.1%
 
4721062.1%
 
Other values (108)7794376.6%
 
ValueCountFrequency (%) 
132083.2%
 
211011.1%
 
36680.7%
 
43780.4%
 
52860.3%
 
ValueCountFrequency (%) 
1321< 0.1%
 
1291< 0.1%
 
1261< 0.1%
 
1211< 0.1%
 
1201< 0.1%
 

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339730362
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Memory size795.0 KiB
2020-12-09T17:24:31.886360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705806979
Coefficient of variation (CV)1.273246489
Kurtosis0.8571103021
Mean1.339730362
Median Absolute Deviation (MAD)1
Skewness1.316414763
Sum136339
Variance2.90977745
MonotocityNot monotonic
2020-12-09T17:24:32.017606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
04665245.8%
 
12074220.4%
 
21271712.5%
 
394439.3%
 
649544.9%
 
441804.1%
 
530783.0%
 
ValueCountFrequency (%) 
04665245.8%
 
12074220.4%
 
21271712.5%
 
394439.3%
 
441804.1%
 
ValueCountFrequency (%) 
649544.9%
 
530783.0%
 
441804.1%
 
394439.3%
 
21271712.5%
 

num_medications
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.02184423
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:32.280795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.127566209
Coefficient of variation (CV)0.5072803163
Kurtosis3.468154915
Mean16.02184423
Median Absolute Deviation (MAD)5
Skewness1.326672134
Sum1630479
Variance66.05733248
MonotocityNot monotonic
2020-12-09T17:24:32.495505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1360866.0%
 
1260045.9%
 
1157955.7%
 
1557925.7%
 
1457075.6%
 
1654305.3%
 
1053465.3%
 
1749194.8%
 
949134.8%
 
1845234.4%
 
Other values (65)4725146.4%
 
ValueCountFrequency (%) 
12620.3%
 
24700.5%
 
39000.9%
 
414171.4%
 
520172.0%
 
ValueCountFrequency (%) 
811< 0.1%
 
791< 0.1%
 
752< 0.1%
 
741< 0.1%
 
723< 0.1%
 

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3693571527
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Memory size795.0 KiB
2020-12-09T17:24:32.721452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.267265097
Coefficient of variation (CV)3.431001911
Kurtosis147.9077363
Mean0.3693571527
Median Absolute Deviation (MAD)0
Skewness8.832958927
Sum37588
Variance1.605960825
MonotocityNot monotonic
2020-12-09T17:24:32.896175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
08502783.6%
 
185478.4%
 
235943.5%
 
320422.0%
 
410991.1%
 
55330.5%
 
63030.3%
 
71550.2%
 
8980.1%
 
9830.1%
 
Other values (29)2850.3%
 
ValueCountFrequency (%) 
08502783.6%
 
185478.4%
 
235943.5%
 
320422.0%
 
410991.1%
 
ValueCountFrequency (%) 
421< 0.1%
 
401< 0.1%
 
391< 0.1%
 
381< 0.1%
 
371< 0.1%
 

number_emergency
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1978362125
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Memory size795.0 KiB
2020-12-09T17:24:33.074957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9304722684
Coefficient of variation (CV)4.703245461
Kurtosis1191.686726
Mean0.1978362125
Median Absolute Deviation (MAD)0
Skewness22.85558215
Sum20133
Variance0.8657786423
MonotocityNot monotonic
2020-12-09T17:24:33.275587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
09038388.8%
 
176777.5%
 
220422.0%
 
37250.7%
 
43740.4%
 
51920.2%
 
6940.1%
 
7730.1%
 
850< 0.1%
 
1034< 0.1%
 
Other values (23)1220.1%
 
ValueCountFrequency (%) 
09038388.8%
 
176777.5%
 
220422.0%
 
37250.7%
 
43740.4%
 
ValueCountFrequency (%) 
761< 0.1%
 
641< 0.1%
 
631< 0.1%
 
541< 0.1%
 
461< 0.1%
 

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6355659061
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Memory size795.0 KiB
2020-12-09T17:24:33.443956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.26286329
Coefficient of variation (CV)1.986990299
Kurtosis20.71939695
Mean0.6355659061
Median Absolute Deviation (MAD)0
Skewness3.614138992
Sum64679
Variance1.594823689
MonotocityNot monotonic
2020-12-09T17:24:33.599359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
06763066.5%
 
11952119.2%
 
275667.4%
 
334113.4%
 
416221.6%
 
58120.8%
 
64800.5%
 
72680.3%
 
81510.1%
 
91110.1%
 
Other values (11)1940.2%
 
ValueCountFrequency (%) 
06763066.5%
 
11952119.2%
 
275667.4%
 
334113.4%
 
416221.6%
 
ValueCountFrequency (%) 
211< 0.1%
 
192< 0.1%
 
181< 0.1%
 
171< 0.1%
 
166< 0.1%
 

diag_1
Categorical

HIGH CARDINALITY

Distinct717
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
428
 
6862
414
 
6581
786
 
4016
410
 
3614
486
 
3508
Other values (712)
77185 
ValueCountFrequency (%) 
42868626.7%
 
41465816.5%
 
78640163.9%
 
41036143.6%
 
48635083.4%
 
42727662.7%
 
49122752.2%
 
71521512.1%
 
68220422.0%
 
43420282.0%
 
Other values (707)6592364.8%
 
2020-12-09T17:24:33.815904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique82 ?
Unique (%)0.1%
2020-12-09T17:24:33.995501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.175215691
Min length1

diag_2
Categorical

HIGH CARDINALITY

Distinct749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
276
 
6752
428
 
6662
250
 
6071
427
 
5036
401
 
3736
Other values (744)
73509 
ValueCountFrequency (%) 
27667526.6%
 
42866626.5%
 
25060716.0%
 
42750364.9%
 
40137363.7%
 
49633053.2%
 
59932883.2%
 
40328232.8%
 
41426502.6%
 
41125662.5%
 
Other values (739)5887757.9%
 
2020-12-09T17:24:34.301828image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique124 ?
Unique (%)0.1%
2020-12-09T17:24:34.488782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.166194996
Min length1

diag_3
Categorical

HIGH CARDINALITY

Distinct790
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
250
11555 
401
8289 
276
 
5175
428
 
4577
427
 
3955
Other values (785)
68215 
ValueCountFrequency (%) 
2501155511.4%
 
40182898.1%
 
27651755.1%
 
42845774.5%
 
42739553.9%
 
41436643.6%
 
49626052.6%
 
40323572.3%
 
58519922.0%
 
27219691.9%
 
Other values (780)5562854.7%
 
2020-12-09T17:24:34.686634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique122 ?
Unique (%)0.1%
2020-12-09T17:24:34.870377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.111658118
Min length1

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.422606765
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size795.0 KiB
2020-12-09T17:24:34.993723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.933600145
Coefficient of variation (CV)0.2605014931
Kurtosis-0.07905602427
Mean7.422606765
Median Absolute Deviation (MAD)1
Skewness-0.8767462388
Sum755369
Variance3.738809521
MonotocityNot monotonic
2020-12-09T17:24:35.184825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
94947448.6%
 
51139311.2%
 
81061610.4%
 
71039310.2%
 
61016110.0%
 
455375.4%
 
328352.8%
 
210231.0%
 
12190.2%
 
1645< 0.1%
 
Other values (6)700.1%
 
ValueCountFrequency (%) 
12190.2%
 
210231.0%
 
328352.8%
 
455375.4%
 
51139311.2%
 
ValueCountFrequency (%) 
1645< 0.1%
 
1510< 0.1%
 
147< 0.1%
 
1316< 0.1%
 
129< 0.1%
 

max_glu_serum
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
None
96420 
Norm
 
2597
>200
 
1485
>300
 
1264
ValueCountFrequency (%) 
None9642094.7%
 
Norm25972.6%
 
>20014851.5%
 
>30012641.2%
 
2020-12-09T17:24:35.378654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:35.481821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:35.610955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
None
84748 
>8
 
8216
Norm
 
4990
>7
 
3812
ValueCountFrequency (%) 
None8474883.3%
 
>882168.1%
 
Norm49904.9%
 
>738123.7%
 
2020-12-09T17:24:35.784212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:35.910048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:36.125024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.763614567
Min length2

metformin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
81778 
Steady
18346 
Up
 
1067
Down
 
575
ValueCountFrequency (%) 
No8177880.4%
 
Steady1834618.0%
 
Up10671.0%
 
Down5750.6%
 
2020-12-09T17:24:36.909607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:37.028446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:37.300656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.732405715
Min length2

repaglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
100227 
Steady
 
1384
Up
 
110
Down
 
45
ValueCountFrequency (%) 
No10022798.5%
 
Steady13841.4%
 
Up1100.1%
 
Down45< 0.1%
 
2020-12-09T17:24:37.520415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:37.649554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:37.849534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.05528369
Min length2

nateglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101063 
Steady
 
668
Up
 
24
Down
 
11
ValueCountFrequency (%) 
No10106399.3%
 
Steady6680.7%
 
Up24< 0.1%
 
Down11< 0.1%
 
2020-12-09T17:24:38.123248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:38.301196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:38.479086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.026472496
Min length2

chlorpropamide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101680 
Steady
 
79
Up
 
6
Down
 
1
ValueCountFrequency (%) 
No10168099.9%
 
Steady790.1%
 
Up6< 0.1%
 
Down1< 0.1%
 
2020-12-09T17:24:38.719651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-09T17:24:38.860423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:39.012061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.003124816
Min length2

glimepiride
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
96575 
Steady
 
4670
Up
 
327
Down
 
194
ValueCountFrequency (%) 
No9657594.9%
 
Steady46704.6%
 
Up3270.3%
 
Down1940.2%
 
2020-12-09T17:24:39.286834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:39.625042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:40.296518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.187371028
Min length2

acetohexamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101765 
Steady
 
1
ValueCountFrequency (%) 
No101765> 99.9%
 
Steady1< 0.1%
 
2020-12-09T17:24:40.757831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-09T17:24:40.986273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:41.182802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039306
Min length2

glipizide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
89080 
Steady
11356 
Up
 
770
Down
 
560
ValueCountFrequency (%) 
No8908087.5%
 
Steady1135611.2%
 
Up7700.8%
 
Down5600.6%
 
2020-12-09T17:24:41.368646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:41.463255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:41.613458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.45736297
Min length2

glyburide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
91116 
Steady
9274 
Up
 
812
Down
 
564
ValueCountFrequency (%) 
No9111689.5%
 
Steady92749.1%
 
Up8120.8%
 
Down5640.6%
 
2020-12-09T17:24:41.809203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:41.931006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:42.100732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.375606784
Min length2

tolbutamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101743 
Steady
 
23
ValueCountFrequency (%) 
No101743> 99.9%
 
Steady23< 0.1%
 
2020-12-09T17:24:42.299929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:42.397743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:42.538407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000904035
Min length2

pioglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
94438 
Steady
 
6976
Up
 
234
Down
 
118
ValueCountFrequency (%) 
No9443892.8%
 
Steady69766.9%
 
Up2340.2%
 
Down1180.1%
 
2020-12-09T17:24:42.706025image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:42.831880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:42.983653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.276516715
Min length2

rosiglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
95401 
Steady
 
6100
Up
 
178
Down
 
87
ValueCountFrequency (%) 
No9540193.7%
 
Steady61006.0%
 
Up1780.2%
 
Down870.1%
 
2020-12-09T17:24:43.203152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:43.306340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:43.461855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.241475542
Min length2

acarbose
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101458 
Steady
 
295
Up
 
10
Down
 
3
ValueCountFrequency (%) 
No10145899.7%
 
Steady2950.3%
 
Up10< 0.1%
 
Down3< 0.1%
 
2020-12-09T17:24:43.653706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:43.760662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:43.914368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.011654187
Min length2

miglitol
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101728 
Steady
 
31
Down
 
5
Up
 
2
ValueCountFrequency (%) 
No101728> 99.9%
 
Steady31< 0.1%
 
Down5< 0.1%
 
Up2< 0.1%
 
2020-12-09T17:24:44.096470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:44.199117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:44.372081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001316746
Min length2

troglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101763 
Steady
 
3
ValueCountFrequency (%) 
No101763> 99.9%
 
Steady3< 0.1%
 
2020-12-09T17:24:44.539978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:44.630858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:44.766988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000117918
Min length2

tolazamide
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101727 
Steady
 
38
Up
 
1
ValueCountFrequency (%) 
No101727> 99.9%
 
Steady38< 0.1%
 
Up1< 0.1%
 
2020-12-09T17:24:44.972810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-09T17:24:45.083420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:45.248295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001493623
Min length2

examide
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101766 
ValueCountFrequency (%) 
No101766100.0%
 
2020-12-09T17:24:45.401591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:45.500319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:45.603550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

citoglipton
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101766 
ValueCountFrequency (%) 
No101766100.0%
 
2020-12-09T17:24:45.828970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:46.394428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:46.879016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

insulin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
47383 
Steady
30849 
Down
12218 
Up
11316 
ValueCountFrequency (%) 
No4738346.6%
 
Steady3084930.3%
 
Down1221812.0%
 
Up1131611.1%
 
2020-12-09T17:24:47.112979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:47.255498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:47.415462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length3.45266592
Min length2
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101060 
Steady
 
692
Up
 
8
Down
 
6
ValueCountFrequency (%) 
No10106099.3%
 
Steady6920.7%
 
Up8< 0.1%
 
Down6< 0.1%
 
2020-12-09T17:24:47.575754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:47.682564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:47.889965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.027317572
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101753 
Steady
 
13
ValueCountFrequency (%) 
No101753> 99.9%
 
Steady13< 0.1%
 
2020-12-09T17:24:48.045643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:48.154079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:48.322580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000510976
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101765 
Steady
 
1
ValueCountFrequency (%) 
No101765> 99.9%
 
Steady1< 0.1%
 
2020-12-09T17:24:48.484058image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-09T17:24:48.579802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:48.726481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039306
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101764 
Steady
 
2
ValueCountFrequency (%) 
No101764> 99.9%
 
Steady2< 0.1%
 
2020-12-09T17:24:48.903181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:48.989346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:49.128099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000078612
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
101765 
Steady
 
1
ValueCountFrequency (%) 
No101765> 99.9%
 
Steady1< 0.1%
 
2020-12-09T17:24:49.495278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-09T17:24:49.636230image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:49.813945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039306
Min length2

change
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
No
54755 
Ch
47011 
ValueCountFrequency (%) 
No5475553.8%
 
Ch4701146.2%
 
2020-12-09T17:24:49.981604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:50.094135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:50.270909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
Yes
78363 
No
23403 
ValueCountFrequency (%) 
Yes7836377.0%
 
No2340323.0%
 
2020-12-09T17:24:50.395761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.0 KiB
NO
54864 
>30
35545 
<30
11357 
ValueCountFrequency (%) 
NO5486453.9%
 
>303554534.9%
 
<301135711.2%
 
2020-12-09T17:24:50.509129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-09T17:24:50.600286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:50.737675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.460880844
Min length2

Interactions

2020-12-09T17:23:41.820475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:42.645546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:43.757505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:44.231993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:44.588476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:44.880462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:45.157661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:45.368881image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:46.015373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:46.219418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:46.393503image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:46.593260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:46.778393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:47.007114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:47.244055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:47.683981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:47.936083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:48.131625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:48.342197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:48.544910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:48.739331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:48.936514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:49.131668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:49.296380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:49.489454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:49.705374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:49.904293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:50.099915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:50.369492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:50.554627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:50.753067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:50.937899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:51.126729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:51.298530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:51.476396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:51.662774image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:51.819807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:52.051746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:52.289823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:52.603367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:52.772811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:52.938703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:53.148950image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:53.339126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:53.520801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:53.716534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:53.881678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:54.053217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:54.236453image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:54.442741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:54.625419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:54.810699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:54.981607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:55.152242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:55.352508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:55.550608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:55.710852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:55.884837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:56.052024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:56.242657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:56.433963image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:56.650483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:56.839028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:57.048130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:57.233154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:57.421551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:57.606921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:57.814085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:58.068304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:58.341297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:58.536986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:58.717778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:58.929348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:59.156752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:59.363800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:59.516907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:59.698743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:23:59.876734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:00.059462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:00.430038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:00.611593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:00.790493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:00.984412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:01.185038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:01.390642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:01.581312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:01.808003image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:01.975271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:02.130052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:02.303476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:02.478243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:02.645040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:02.824862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:03.031170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:03.211152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:03.389836image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:03.576308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:03.738823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:03.908031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:04.107044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:04.283789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:04.449543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:04.623290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:04.775201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:04.952584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:05.116899image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:05.305713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:05.500797image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:05.671498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:05.842803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:06.017734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:06.201799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:06.401132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:06.573184image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:06.737437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:06.975915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:07.216976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:07.395446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:07.544750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:07.712211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:07.878539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:08.032414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:08.194855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:08.350468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:08.510713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:08.689347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:08.857247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:09.235564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:09.406181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:09.586386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:09.988584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:10.210772image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:10.437798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:10.609747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:10.861430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:11.078506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:11.277546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:11.476307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:11.657906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:11.865938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:12.091013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:12.622414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:12.855398image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:13.149616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:13.441496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:13.687810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:13.897670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:14.057743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:14.266612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:14.444364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:14.604118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:14.773987image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:14.961924image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:15.134177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:15.342330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:15.548605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:15.728637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:15.940580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:16.177622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:16.396313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:16.753637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:16.982523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:17.296734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:17.497157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:17.689099image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:18.855030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:19.074791image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:19.491889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:19.682009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-12-09T17:24:50.904278image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-09T17:24:51.322362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-09T17:24:51.579516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-09T17:24:52.106855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-09T17:24:52.693658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-09T17:24:20.912670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-09T17:24:23.751955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
022783928222157CaucasianFemale[0-10)?62511?Pediatrics-Endocrinology4101000250.83??1NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO
114919055629189CaucasianFemale[10-20)?1173??59018000276250.012559NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYes>30
26441086047875AfricanAmericanFemale[20-30)?1172??11513201648250V276NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYesNO
350036482442376CaucasianMale[30-40)?1172??441160008250.434037NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
41668042519267CaucasianMale[40-50)?1171??51080001971572505NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
53575482637451CaucasianMale[50-60)?2123??316160004144112509NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
65584284259809CaucasianMale[60-70)?3124??70121000414411V457NoneNoneSteadyNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
763768114882984CaucasianMale[70-80)?1175??730120004284922508NoneNoneNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYes>30
81252248330783CaucasianFemale[80-90)?21413??68228000398427388NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
91573863555939CaucasianFemale[90-100)?33412?InternalMedicine333180004341984868NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO

Last rows

encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
101756443842070140199494OtherFemale[60-70)?1172MD?466171119965854039NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
101757443842136181593374CaucasianFemale[70-80)?1175??211160014915185119NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
101758443842340120975314CaucasianFemale[80-90)?1175MC?7612201029283049NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10175944384277886472243CaucasianMale[80-90)?1171MC?10153004357842507NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10176044384717650375628AfricanAmericanFemale[60-70)?1176DM?451253123454384129NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
101761443847548100162476AfricanAmericanMale[70-80)?1373MC?51016000250.132914589None>8SteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
10176244384778274694222AfricanAmericanFemale[80-90)?1455MC?333180015602767879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
10176344385414841088789CaucasianMale[70-80)?1171MC?53091003859029613NoneNoneSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYesNO
10176444385716631693671CaucasianFemale[80-90)?23710MCSurgery-General452210019962859989NoneNoneNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
101765443867222175429310CaucasianMale[70-80)?1176??13330005305307879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO